CN110097105A - A kind of digestive endoscopy based on artificial intelligence is checked on the quality automatic evaluation method and system - Google Patents
A kind of digestive endoscopy based on artificial intelligence is checked on the quality automatic evaluation method and system Download PDFInfo
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Abstract
The present invention provides a kind of digestive endoscopy based on artificial intelligence and checks on the quality automatic evaluation method and system, it is therefore intended that it is accurate, comprehensively, the gastrointestinal endoscopy quality of quick assessment medical institutions, provide practicable inspection foundation to improve digestive endoscopy quality.It checks on the quality and assess and showing to digestion endoscope diagnosis and treatment mechanism through the invention, on the one hand objective directly to express the current operation horizontal quantitativeization of doctor, excitation scope doctor learns from each other, and it is horizontal that operation is continuously improved.On the other hand the quality report for the digestive endoscopy diagnosis and treatment mechanism that higher level's medical control platform can also be allowed all-sidedly and accurately to obtain in administration, does good quality control in time.
Description
Technical field
The invention belongs to medical technology field of auxiliary, and in particular to a kind of digestive endoscopy based on artificial intelligence is checked on the quality
Automatic evaluation method.
Background technique
Endoscopy is to find the most common powerful of human primary gastrointestinal cancers, and since reform and opening-up, China's digestive endoscopy industry is fast
Speed development.However, also reality exists for some quality of medical care and security risk in the behind that technology flourishes.Different scope doctors
The level of teacher is irregular, checks on the quality and is difficult to be completely secured.It checks on the quality to improve China's digestive endoscopy, country defends strong
Committee and each domain experts of digestive endoscopy have paid a large amount of effort.Quality Control measure used at present includes expert group's selective examination, people
Work investigation and hospital voluntarily report etc. that there are certain drawbacks, cannot achieve the comprehensive assessment of digestive endoscopy quality, data are accurate
Property is difficult to be completely secured.In addition, visiting medical institutions at different levels by QCC quality control center member and digestion expert and doing artificial tune
It looks into, it is time- and labor-consuming, and surprise check can not objectively respond the daily treatment level of medical institutions.
In recent years science and technology grow rapidly, artificial intelligence be used successfully to monitoring driving behavior, analyze driver strategy and
State, and Real-time Feedback and sounded an alarm in occurrence risk.In food service industry, artificial intelligence has been successfully applied to food matter
Monitoring and warning system is measured, however, there is no the application study checked on the quality using artificial intelligence monitoring digestive endoscopy.Based on this, I
Send out a kind of bright digestive endoscopy based on artificial intelligence and check on the quality automatic evaluation method, it is accurate, comprehensively, quickly assessment
The gastrointestinal endoscopy quality of medical institutions provides practicable inspection foundation to improve digestive endoscopy quality.
Summary of the invention
The technical problem to be solved by the present invention is providing a kind of digestive endoscopy assessment tool of intelligent and high-efficiency, science is established
Precisian's work intelligent evaluation system.
A kind of technical solution taken by the invention to solve the above technical problem are as follows: digestive endoscopy based on artificial intelligence
It checks on the quality assessment system, comprising:
Medical image reporting system: doctor is manually entered patient information, and acquires endoscopic image, note by stomach and intestine mirror device
Record the corresponding operating time；
Pre-training model based on big data: collecting a large amount of endoscopic image, by professional scope doctor according to the dissection department of the Chinese Academy of Sciences
Position and focus characteristic carry out classification annotation to image, construct pretreatment image collection, by based on parameter/feature transfer learning,
Using pretreatment image collection training depth convolutional neural networks model, obtain recognizing the pre- of alimentary canal position and lesion automatically
Training pattern；
Quality Control Software of Data Statistics: the calculation formula based on endoscopic technic quality control index, using from medical image
The written historical materials of reporting system and from pre-training model for the prediction result of picture, it is automatic to calculate the multinomial of endoscopic technic
Quality control index；
Wherein, the quality control index includes the gastrocopy time, gastrocopy blind area rate, colonoscopy cecal intubation rate,
Colonoscopy moves back the mirror time, colorectal polypus recall rate, Colon and rectum adenoma recall rate, INTESTINAL CLEANSING success rate, the discovery of alimentary canal morning cancer
Rate；
User: including doctor and manager, for checking the statistical result of Quality Control Software of Data Statistics output.
Further, the gastrocopy time is specifically defined are as follows: the time from endoscope insertion tube to tube drawing, calculation formula
Are as follows: the time of gastrocopy time=gastroscope end time-gastroscope time started-carry out biopsy or treatment.
Further, gastrocopy blind area rate is specifically defined are as follows: the position that gastroscope does not check accounts for 26 position of gastroscope
Ratio, calculation formula are as follows: the gastroscope lower part digit that gastrocopy blind area rate=1- is observed/gastroscope lower portion sum ×
100%.
Further, colonoscopy cecal intubation rate is specifically defined are as follows: enteroscopy cecal intubation success number of cases accounts for same period intestines
The ratio of spectroscopy sum, calculation formula are as follows: colonoscopy cecal intubation rate=enteroscopy cecal intubation success number of cases/same period intestines
Spectroscopy sum × 100%.
Further, colonoscopy moves back being specifically defined for mirror time are as follows: in colonoscopy procedures, since reaching caecum into mirror
It does not include the time to operation bidirectionals such as polyp progress biopsies, i.e., negative Sigmoidoscope to mirror is moved back to the real time between rectum
Moving back mirror time, calculation formula are as follows: colonoscopy moves back the mirror time ,=the when m- discovery adenoma of mirror to rectum is moved back from caecum/carries out polyp
The time of biopsy or treatment.
Further, colorectal polypus recall rate is specifically defined are as follows: one piece of Colon and rectum breath is at least detected in enteroscopy
Patient's number of meat accounts for the ratio of same period colonoscopy sum, calculation formula are as follows: colorectal polypus recall rate=colonoscopy
Polyp case load/same period all colonoscopy sum × 100% out.
Further, Colon and rectum adenoma recall rate is specifically defined are as follows: at least detects one piece of Colon and rectum gland in enteroscopy
Patient's number of tumor accounts for the ratio of same period colonoscopy sum, calculation formula are as follows: Colon and rectum adenoma recall rate=colonoscopy
Adenoma case load/same period all colonoscopy sum × 100% out.
Further, INTESTINAL CLEANSING success rate is specifically defined are as follows: is had a small amount of in enteric cavity or is not influenced without excrement slag and intestines
Patient's number of sem observation accounts for the ratio of same period colonoscopy sum, calculation formula are as follows: in INTESTINAL CLEANSING success rate=1- enteric cavity
Growing number/same period colonoscopy sum × 100% comprising not conforming to table images.
Further, alimentary canal morning cancer discovery rate is specifically defined are as follows: gastrointestinal endoscopy find the early stage cancer of the esophagus, gastric cancer or
Patient's number of colorectal cancer accounts for the ratio of the same period cancer of the esophagus, gastric cancer or colorectal cancer patients number, calculation formula respectively are as follows: digestion
Road morning cancer discovery rate=gastrointestinal endoscopy finds the patient's number/morning same period cancer and middle and advanced stage cancer patient populations × 100% of early cancer.
It checks on the quality the method assessed automatically the present invention also provides a kind of digestive endoscopy based on artificial intelligence, including as follows
Step:
S1, doctor are manually entered patient information, acquisition endoscopic image, automatically record patient's letter by image report system
Breath, collected picture and corresponding operation time；
S2 obtains patient information, collected picture and corresponding operation time by data exchange agreement, wherein text
Data input Quality Control Software of Data Statistics, picture information input depth convolutional neural networks model；
S3, depth convolutional neural networks model receive Gastrointestinal Endoscopes image, carry out genius loci to image and focus characteristic is known
Not, prediction result is inputted into Quality Control Software of Data Statistics；
S4, Quality Control Software of Data Statistics is according to the calculation formula of endoscopic technic quality control index, using coming from medicine shadow
It is automatic to calculate the more of endoscopic technic as the written historical materials of reporting system and from pre-training model for the prediction result of picture
Item quality control index；
Wherein, the quality control index includes the gastrocopy time, gastrocopy blind area rate, colonoscopy cecal intubation rate,
Colonoscopy moves back the mirror time, colorectal polypus recall rate, Colon and rectum adenoma recall rate, INTESTINAL CLEANSING success rate, the discovery of alimentary canal morning cancer
Rate；
S5, Quality Control Software of Data Statistics are exported statistical result by forms such as charts, and doctor and manager can voluntarily look into
See statistical result.
The invention has the benefit that through the invention to digestion endoscope diagnosis and treatment mechanism check on the quality carry out assessment and into
Row display, on the one hand objective directly to express the current operation horizontal quantitativeization of doctor, excitation scope doctor learns from each other,
It is horizontal that operation is continuously improved.On the other hand higher level's medical control platform can also be allowed all-sidedly and accurately to obtain the digestion in administration
The quality report of endoscope diagnosis and treatment mechanism, does good quality control in time.
Detailed description of the invention
Fig. 1 is system construction drawing of the invention.
Specific embodiment
Technical solution of the present invention is described further with reference to the accompanying drawings and examples.
The assessment system as shown in Figure 1, a kind of digestive endoscopy based on artificial intelligence that the present invention develops is checked on the quality, it is wrapped
It includes:
Medical image reporting system: doctor is manually entered patient information, and acquires endoscopic image by stomach and intestine mirror device.Doctor
The medical image reporting system of institute can automatically record patient information, collected picture and corresponding operation time.
Pre-training model based on big data: collecting a large amount of endoscopic image, by professional scope doctor according to the dissection department of the Chinese Academy of Sciences
Position and focus characteristic carry out classification annotation to image, construct pretreatment image collection.By based on parameter/feature transfer learning,
Using pretreatment image collection training pattern (the general depth convolutional neural networks model using in deep learning), obtaining can be certainly
The pre-training model at dynamic identification alimentary canal position and lesion.
Quality Control Software of Data Statistics: the calculation formula based on endoscopic technic quality control index, using from medical image
The written historical materials of reporting system and from pre-training model for the prediction result of picture, it is automatic to calculate the multinomial of endoscopic technic
Quality control index.
User: including doctor and manager, user can check the statistical result of Quality Control Software of Data Statistics output.
It checks on the quality the method assessed automatically the present invention also provides a kind of digestive endoscopy based on artificial intelligence, including following
Step:
S1. doctor is manually entered patient information, acquisition endoscopic image.The image report system of hospital automatically records patient's letter
Breath, collected picture and corresponding operation time；
S2. patient information, collected picture and corresponding operation time are obtained by data exchange agreement, wherein text
Data input Quality Control Software of Data Statistics, picture information input the pre-training model based on big data；
S3. pre-training model receives Gastrointestinal Endoscopes image, carries out genius loci to image and focus characteristic identifies, prediction is tied
Fruit inputs Quality Control Software of Data Statistics；
S4. Quality Control Software of Data Statistics is according to the calculation formula of endoscopic technic quality control index, using coming from medicine shadow
It is automatic to calculate the more of endoscopic technic as the written historical materials of reporting system and from pre-training model for the prediction result of picture
Item quality control index.
The multinomial quality control index counted includes but is not limited to: the gastrocopy time, gastrocopy blind area rate, colonoscopy
Cecal intubation rate, colonoscopy move back the mirror time, colorectal polypus recall rate, and Colon and rectum adenoma recall rate, INTESTINAL CLEANSING success rate disappears
Change road morning cancer discovery rate；
S5. Quality Control Software of Data Statistics is exported statistical result by forms such as charts, and doctor and manager can voluntarily look into
See statistical result.
Wherein gastrocopy time, gastrocopy blind area rate, colonoscopy cecal intubation rate, colonoscopy move back the mirror time, Colon and rectum breath
Meat recall rate, Colon and rectum adenoma recall rate, INTESTINAL CLEANSING success rate, being specifically defined of alimentary canal morning cancer discovery rate, calculation formula
And realize that step is identical with the specific implementation in Quality Control Software of Data Statistics.
Specific embodiment described herein is only an example for the spirit of the invention.The neck of technology belonging to the present invention
The technical staff in domain can make various modifications or additions to the described embodiments or replace by a similar method
In generation, however, it does not deviate from the spirit of the invention or beyond the scope of the appended claims.
Claims (10)
- The assessment system 1. a kind of digestive endoscopy based on artificial intelligence is checked on the quality characterized by comprisingMedical image reporting system: doctor is manually entered patient information, and acquires endoscopic image by stomach and intestine mirror device, records phase The operating time answered；Pre-training model based on big data: collecting a large amount of endoscopic image, by professional scope doctor according to anatomical site and Focus characteristic carries out classification annotation to image, constructs pretreatment image collection, by using based on parameter/feature transfer learning Pretreatment image collection training depth convolutional neural networks model, obtains the pre-training mould that can recognize alimentary canal position and lesion automatically Type；Quality Control Software of Data Statistics: the calculation formula based on endoscopic technic quality control index is reported using from medical image The written historical materials of system and from pre-training model for the prediction result of picture, the automatic multinomial quality control for calculating endoscopic technic Index processed；Wherein, the quality control index includes the gastrocopy time, gastrocopy blind area rate, colonoscopy cecal intubation rate, colonoscopy It moves back the mirror time, colorectal polypus recall rate, Colon and rectum adenoma recall rate, INTESTINAL CLEANSING success rate, alimentary canal morning cancer discovery rate；User: including doctor and manager, for checking the statistical result of Quality Control Software of Data Statistics output.
- The assessment system 2. a kind of digestive endoscopy based on artificial intelligence as described in claim 1 is checked on the quality, it is characterised in that: The gastrocopy time is specifically defined are as follows: the time from endoscope insertion tube to tube drawing, calculation formula are as follows: the gastrocopy time=stomach The time of mirror end time-gastroscope time started-carry out biopsy or treatment.
- The assessment system 3. a kind of digestive endoscopy based on artificial intelligence as described in claim 1 is checked on the quality, it is characterised in that: Gastrocopy blind area rate is specifically defined are as follows: the position that gastroscope does not check accounts for the ratio at 26 position of gastroscope, calculation formula are as follows: stomach The gastroscope lower part digit that spectroscopy blind area rate=1- is observed/gastroscope lower portion sum × 100%.
- The assessment system 4. a kind of digestive endoscopy based on artificial intelligence as described in claim 1 is checked on the quality, it is characterised in that: Colonoscopy cecal intubation rate is specifically defined are as follows: and enteroscopy cecal intubation success number of cases accounts for the ratio of same period enteroscopy sum, Calculation formula are as follows: colonoscopy cecal intubation rate=enteroscopy cecal intubation success number of cases/same period enteroscopy sum × 100%.
- The assessment system 5. a kind of digestive endoscopy based on artificial intelligence as described in claim 1 is checked on the quality, it is characterised in that: Colonoscopy moves back being specifically defined for mirror time are as follows: in colonoscopy procedures, to moving back mirror to rectum since reaching caecum into mirror Real time, do not include the time that the operation bidirectionals such as biopsy are carried out to polyp, i.e., negative Sigmoidoscope moves back mirror time, calculation formula Are as follows: colonoscopy moves back the mirror time, and=the when m- discovery adenoma of mirror to rectum is moved back from caecum/carries out the time of biopsy or treatment to polyp.
- The assessment system 6. a kind of digestive endoscopy based on artificial intelligence as described in claim 1 is checked on the quality, it is characterised in that: Colorectal polypus recall rate is specifically defined are as follows: patient's number that one piece of colorectal polypus is at least detected in enteroscopy accounts for same period knot The ratio of enteroscopy sum, calculation formula are as follows: colorectal polypus recall rate=colonoscopy goes out polyp case load/same period institute There is colonoscopy sum × 100%.
- The assessment system 7. a kind of digestive endoscopy based on artificial intelligence as described in claim 1 is checked on the quality, it is characterised in that: Colon and rectum adenoma recall rate is specifically defined are as follows: patient's number that one piece of Colon and rectum adenoma is at least detected in enteroscopy accounts for same period knot The ratio of enteroscopy sum, calculation formula are as follows: Colon and rectum adenoma recall rate=colonoscopy finds adenhomatosis number of cases/same period institute There is colonoscopy sum × 100%.
- The assessment system 8. a kind of digestive endoscopy based on artificial intelligence as described in claim 1 is checked on the quality, it is characterised in that: INTESTINAL CLEANSING success rate is specifically defined are as follows: is had a small amount of in enteric cavity or is not influenced without excrement slag and patient's number of colonoscopy observation and account for together The ratio of phase colonoscopy sum, calculation formula are as follows: include the case for not conforming to table images in INTESTINAL CLEANSING success rate=1- enteric cavity Number of cases amount/same period colonoscopy sum × 100%.
- The assessment system 9. a kind of digestive endoscopy based on artificial intelligence as described in claim 1 is checked on the quality, it is characterised in that: Alimentary canal morning cancer discovery rate is specifically defined are as follows: gastrointestinal endoscopy finds patient's number of the early stage cancer of the esophagus, gastric cancer or colorectal cancer The ratio of the same period cancer of the esophagus, gastric cancer or colorectal cancer patients number, calculation formula are accounted for respectively are as follows: alimentary canal morning cancer discovery rate=stomach and intestine Spectroscopy finds the patient's number/morning same period cancer and middle and advanced stage cancer patient populations × 100% of early cancer.
- A kind of method assessed automatically 10. digestive endoscopy based on artificial intelligence is checked on the quality, which is characterized in that including walking as follows It is rapid:S1, doctor are manually entered patient information, acquisition endoscopic image, automatically record patient information, quilt by image report system The picture of acquisition and corresponding operation time；S2 obtains patient information, collected picture and corresponding operation time by data exchange agreement, wherein written historical materials Quality Control Software of Data Statistics is inputted, picture information inputs depth convolutional neural networks model；S3, depth convolutional neural networks model receive Gastrointestinal Endoscopes image, carry out genius loci to image and focus characteristic identifies, will Prediction result inputs Quality Control Software of Data Statistics；S4, Quality Control Software of Data Statistics is according to the calculation formula of endoscopic technic quality control index, using coming from medical image report The written historical materials of announcement system and from pre-training model for the prediction result of picture, the automatic multinomial quality for calculating endoscopic technic Con trolling index；Wherein, the quality control index includes the gastrocopy time, gastrocopy blind area rate, colonoscopy cecal intubation rate, colonoscopy It moves back the mirror time, colorectal polypus recall rate, Colon and rectum adenoma recall rate, INTESTINAL CLEANSING success rate, alimentary canal morning cancer discovery rate；S5, Quality Control Software of Data Statistics are exported statistical result by forms such as charts, and doctor and manager voluntarily check statistics As a result.
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